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Simulated tempering yields insight into the low‐resolution Rosetta scoring functions
Author(s) -
Bowman Gregory R.,
Pande Vijay S.
Publication year - 2008
Publication title -
proteins: structure, function, and bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.699
H-Index - 191
eISSN - 1097-0134
pISSN - 0887-3585
DOI - 10.1002/prot.22210
Subject(s) - sampling (signal processing) , maxima and minima , computer science , resolution (logic) , convergence (economics) , algorithm , parallel tempering , state (computer science) , phase space , low resolution , state space , artificial intelligence , statistical physics , data mining , high resolution , mathematics , physics , statistics , remote sensing , geography , telecommunications , thermodynamics , mathematical analysis , bayesian probability , detector , markov chain monte carlo , monte carlo molecular modeling , economics , economic growth
Rosetta is a structure prediction package that has been employed successfully in numerous protein design and other applications.1 Previous reports have attributed the current limitations of the Rosetta de novo structure prediction algorithm to inadequate sampling, particularly during the low‐resolution phase.2–5 Here, we implement the Simulated Tempering (ST) sampling algorithm6, 7 in Rosetta to address this issue. ST is intended to yield canonical sampling by inducing a random walk in temperatures space such that broad sampling is achieved at high temperatures and detailed exploration of local free energy minima is achieved at low temperatures. ST should therefore visit basins in accordance with their free energies rather than their energies and achieve more global sampling than the localized scheme currently implemented in Rosetta. However, we find that ST does not improve structure prediction with Rosetta. To understand why, we carried out a detailed analysis of the low‐resolution scoring functions and find that they do not provide a strong bias towards the native state. In addition, we find that both ST and standard Rosetta runs started from the native state are biased away from the native state. Although the low‐resolution scoring functions could be improved, we propose that working entirely at full‐atom resolution is now possible and may be a better option due to superior native‐state discrimination at full‐atom resolution. Such an approach will require more attention to the kinetics of convergence, however, as functions capable of native state discrimination are not necessarily capable of rapidly guiding non‐native conformations to the native state. Proteins 2009. © 2008 Wiley‐Liss, Inc.